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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 847 records · Page 47
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Photoresponsive polymers for carbodiimide-fueled transient hydrogels

Light- and carbodiimide-responsive hydrogels were synthesized. An “AND” gate was developed using ortho-nitrobenzyl (ONB) protected carboxylic acids in the polymer backbone. Crosslinking was only realized in the presence of both UV stimulus to photocleave the ONB group and carbodiimide fuels to induce anhydride bonds. In the presence of water, the anhydride bonds eventually hydrolyze to carboxylic acids and the system returns to the solution state. The mechanical properties of the out-of-equilibrium hydrogels were investigated using oscillatory rheology to examine the effects of deprotection efficiency, carbodiimide concentration and chain architecture on the hydrogels’ moduli and decrosslinking time. Higher moduli and longer decrosslinking times were found with increased carbodiimide concentration and deprotection efficiency. These discoveries unveil new possibilities for photoresponsive chemically fueled soft materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Effect of boron concentration on local structure and spontaneous polarization in AlBN thin films

The discovery of ferroelectricity in polar wurtzite-based ternary materials, such as Al 1–x B x N, has attracted attention due to their compatibility with complementary metal–oxide–semiconductor processes and potential use in integrated non-volatile memory devices. However, the origin of ferroelectricity and the fundamental control of the polarization switching in these materials are still under intensive investigation but appear to be related to local disorder induced from the alloying. In this work, we report the effect of boron alloying on the local structure of Al 1–x B x N films deposited by magnetron sputtering. Our results reveal a diminished crystalline order as a function of boron concentration, accompanied by a reduction in the spontaneous polarization. The film disorder is primarily associated with the dissimilar bond lengths between Al–N and B–N and the formation of threading dislocations induced by B incorporation in the structure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Interwoven atypical quantum states in CeLiBi 2

Here we report the discovery of CeLiBi 2 , the first example of a material in the tetragonal CeTX 2 ( T = transition metal; X = pnictogen) family wherein an alkali cation replaces the typical transition metal. Magnetic susceptibility and neutron powder diffraction measurements are consistent with a crystal-field Γ 6 ground-state Kramers doublet that orders antiferromagnetically below T N = 3.4 K with an incommensurate propagation wave vector k = ( 0, 0.0724(4), 0.5) that generates a nanometric modulation of the magnetic structure. The best model of the ordered state is an elliptical cycloid with Ce moments primarily residing in the ab plane. This is highly unusual, as all other Γ 6 CeTX 2 members order ferromagnetically. Further, we observe an atypical hard-axis metamagnetic transition at 2 T in magnetostriction, magnetization, and resistivity measurements. CeLiBi 2 is a rare example of a highly conductive material with dominant skew scattering leading to a large anomalous Hall effect. Quantum oscillations with five frequencies arise in magnetostriction and magnetic susceptibility data to T = 30 K and μ 0 H = 55 T, which indicate small Fermi pockets of light carriers with effective masses as low as 0.07 m e . Density functional theory calculations indicate that square-net Dirac-like Bi-p bands are responsible for these ultralight carriers. Together, our results show that CeLiBi 2 enables multiple atypical magnetic and electronic properties in a single clean material.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Chemical classification program synthesis using generative artificial intelligence

Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.

Artificial Intelligence↗
DOE OSTI2023

Enhancement of second-order optical nonlinearities and nanoscale periodic domain patterning in ferroelectric boron-substituted aluminum nitride thin films

The discovery and development of CMOS-compatible, nonlinear optical materials is essential to produce integrated photonic devices with advanced functionalities. AlN is a strong candidate for on-chip device demonstration due to its intrinsic second-order optical nonlinearities, large bandgap, and well-established fabrication techniques. However, AlN is not easily phase matched for the largest coefficient d 33 ; the coefficients that could potentially be dispersion phase-matched, d 31 and d 15 , have weak nonlinearities. This work investigates ferroelectric Al 1-x B x N (x = 0 to 0.11) for viability as a large bandgap nonlinear optical material with unique suitability towards ultraviolet light generation using second harmonic generation. The linear and nonlinear optical properties are characterized accounting for material anisotropy. With increasing B concentration, a large enhancement from near negligible values to d 31 = 0.9 ± 0.1 pm/V and d 15 = 1.2 ± 0.1 pm/V is observed. This compares favorably to other large bandgap materials like β-Ba(BO 2 ) 2 , where the largest nonlinear coefficient is d 22 ~ 2.3 pm/V at 800 nm. This is accompanied by a change in the bandgap from 6.1 eV to 5.8 eV as B substitution goes from 0 to 11%. A periodically poled, quasi-phase-matched ferroelectric domain pattern with 400 nm domain size and a wall roughness of <16 nm is demonstrated.

36 MATERIALS SCIENCE↗
DOE OSTI2023

High gradient research and facilities at Los Alamos National Laboratory [Slides]

A particle accelerator is a scientific instrument that produces a directional stream of electrically charged particles, usually electrons or protons. The accelerator propels particles to high speeds, close to the speed of light. Historically, accelerators play a crucial role in scientific discovery. Nowadays, accelerators produce particle beams for biomedical and material research, manufacturing, energy technology, and Homeland security.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Generative Electrolyte Solvent and Formulation Discovery

Molecular mixtures and/or formulations are of great importance in fields ranging from materials science to pharmaceuticals to chemistry. In batteries, electrolytes are complex molecular mixtures consisting of multiple salts and solvents and additives at different concentrations that dictate battery capacity, safety, and cycle life, among others. Unfortunately, due to the complex composition and infinite design space as well as the conflicting property requirements, electrolyte design is the rate-determining step in the design of next generation battery chemistries. In this work, we develop a transformer-based generative AI model − ElectrolyteGPT − capable of generating solvents and electrolyte formulations to satisfy a wide range of desired property requirements. First, we curate an electrolyte-relevant database and develop a new line notation for formulations. Then, we show that ElectrolyteGPT can generate solvents and formulations conditioned on a wide range of important electrolyte properties such as ionic conductivity, oxidative stability, Coulombic efficiency, viscosity, and more. Finally, we experimentally synthesize the generated solvents and fabricate the electrolyte formulations and show that they can meet the desired property requirements and enable longterm cycling in energy-dense anode-free lithium metal batteries. Our work showcases the ability of generative models to address challenges in molecular mixture design for next generation batteries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Discovery of Charge Order and Corresponding Edge State in Kagome Magnet FeGe

Kagome materials often host exotic quantum phases, including spin liquids, Chern gap, charge density wave, and superconductivity. Existing scanning microscopy studies of the kagome charge order have been limited to nonkagome surface layers. Here, we tunnel into the kagome lattice of FeGe to uncover features of the charge order. Our spectroscopic imaging identifies a 2×2 charge order in the magnetic kagome lattice, resembling that discovered in kagome superconductors. Spin mapping across steps of unit cell height demonstrates the existence of spin-polarized electrons with an antiferromagnetic stacking order. We further uncover the correlation between antiferromagnetism and charge order anisotropy, highlighting the unusual magnetic coupling of the charge order. Finally, we detect a pronounced edge state within the charge order energy gap, which is robust against the irregular shape fluctuations of the kagome lattice edges. Here, we discuss our results with the theoretically considered topological features of the kagome charge order including unconventional magnetism and bulk-boundary correspondence.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Novel color center platforms enabling fundamental scientific discovery

Abstract Color centers are versatile systems that generate quantum light, sense magnetic fields and produce spin‐photon entanglement. We review how these properties have pushed the limits of fundamental knowledge in a variety of scientific disciplines, from rejecting local‐realistic theories to sensing superconducting phase transitions. In the light of recent progress in material processing and device fabrication, we identify new opportunities for interdisciplinary fundamental discoveries in physics and geochemistry. image

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

STEM Imaging of Materials: Final Report for DE-FG02-08ER46547

This project supported research advancing the state of the art in scanning transmission electron microscopy (STEM) imaging and characterization of materials, especially electronic materials. The project began with studies of point defects in materials using high-resolution Z-contrast STEM, then evolved to including methods in four-dimensional (4D) STEM and machine learning. Highlights include: (1) the discovery of stable, p-type ZnO via Sb doping, (2) demonstration that the high performance of InGaN LEDs is not due to In composition fluctuations as previously proposed, (3) the demonstration of the first sub-pm precision high-resolution STEM images using non-rigid registration of a series of STEM images, (4) the extension of high-precision STEM to low dose imaging and spectrum imaging, (5) discovery of a synthesis method for twisted spiral growth of transition-metal dichalcogenide materials via chemical vapor deposition, and (6) development of machine-learning methods for analysis and denoising of enormous 4D STEM data sets.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Uncovering fast solid-acid proton conductors based on dynamics of polyanion groups and proton bonding strength

Achieving high proton conductivity in inorganic solids is key for advancing many electrochemical technologies, including low-energy nano-electronics and energy-efficient fuel cells and electrolyzers. A quantitative understanding of the physical traits of a material that regulate proton diffusion is necessary for accelerating the discovery of fast proton conductors. In this work, we have mapped the structural, chemical and dynamic properties of solid acids to the elementary steps of the Grotthuss mechanism of proton diffusion. Our approach combines ab initio molecular dynamics simulations, analysis of phonon spectra and atomic structure calculations. We have identified the donor–hydrogen bond lengths and the acidity of polyanion groups as key descriptors of local proton transfer and the vibrational frequencies of the cation framework as the key descriptor of lattice flexibility. The latter facilitates rotations of polyanion groups and long-range proton migration in solid acid proton conductors. The calculated lattice flexibility also correlates with the experimentally reported superprotonic transition temperatures. Using these descriptors, we have screened the Materials Project database and identified potential solid acid proton conductors with monovalent, divalent and trivalent cations, including Ag + , Sr 2+ , Ba 2+ and Er 3+ cations, which go beyond the traditionally considered monovalent alkali cations (Cs + , Rb + , K + , and NH 4 + ) in solid acids.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Observation of spin-momentum locked surface states in amorphous Bi 2 Se 3

Crystalline symmetries have played a central role in the identification and understanding of quantum materials. Here we investigate whether an amorphous analogue of a well-known three-dimensional strong topological insulator has topological properties in the solid state. We show that amorphous Bi 2 Se 3 thin films host a number of two-dimensional surface conduction channels. Our angle-resolved photoemission spectroscopy data are consistent with a dispersive two-dimensional surface state that crosses the bulk gap. Spin-resolved photoemission spectroscopy shows this state has an anti-symmetric spin texture, confirming the existence of spin-momentum locked surface states. We discuss these experimental results in light of theoretical photoemission spectra obtained with an amorphous topological insulator tight-binding model, contrasting it with alternative explanations. In conclusion, the discovery of spin-momentum locked surface states in amorphous materials opens a new avenue to characterize amorphous matter and triggers the search for an overlooked subset of quantum materials outside of current classification schemes.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Modeling, analysis, and optimization of complex nuclear processes and facilities via computational methods: The HALEU process case study

Improving and adapting industrial systems to timely meet changing programmatic and market demands is an important goal to achieve, including when operating and maintaining complex nuclear processes and facilities. However, changes to these complex systems are costly, particularly when they are already in place and bounded to stringent requirements and constraints such as when handling radioactive material and contaminated equipment. These conditions often exist when treating spent nuclear fuel remotely within shielded nuclear radiation chambers, commonly referred as hot cells, to condition nuclear material and/or fabricate products for utilization in other nuclear enterprises such as in the manufacture of advanced nuclear fuel. The illustrative case considered here is the production of high assay low enriched uranium (HALEU) products supporting the deployment of advanced nuclear reactors. For the HALEU program, resources invested were and are being systematically analyzed so that these investments are maximized in a facility that is nearly 60 years old. A methodology that has effectively enabled optimized and improvements in the Spent Fuel Treatment (SFT) program, and consequently the HALEU program, involves discrete event simulation as addressed in this article. Here, the quantification of multiple productivity metrics, including material processing rates, cycle times, bottlenecks, number of material transfers as well as equipment, workstation, and material handling utilization, has resulted in a myriad of diverse discoveries and data-informed decisions regarding process layout and constituent, labor levels and schedules, selection of new process units, storage needs, and other critical process configurations. This article describes such a computational capability being applied for decision-making, illustrates its application to an actual process and program, provides illustrative results, and argues how computational methods for the modeling, analysis, and optimization of complex processes and facilities does lead to informed decisions derived from data and not only from intuition.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

The Rise of Intelligent Materials Science: Unleashing the Power of Machine Intelligence in Characterization

Machine intelligence has the potential to revolutionize materials science, enabling autonomous synthesis, self-driving characterization, and accelerated modeling. However, despite the promise, successful implementation of these methods in day-to-day research remains a challenge. This talk will delve into the reasons behind this, exploring how truly intelligent experiments are hindered by opaque experiment control, a lack of domain-specific models, and human-centric design. Through a focus on the characterization of next-generation microelectronics and energy storage materials, I will share insights from both successful and failed attempts to implement machine intelligence. We will then explore the next steps necessary to unlock the full potential of machine intelligence in materials science, creating a future where intelligent systems work seamlessly alongside researchers to drive innovation and discovery.

autonomous↗
DOE OSTI2025

Interwoven magnetic kagome metal overcomes geometric frustration

Magnetic kagome materials provide a platform for exploring magneto-transport phenomena, symmetry breaking and charge ordering driven by the intricate interplay among electronic structure, topology and magnetism. Yet geometric frustration in conventional kagome magnets limits their tunability. Here we propose a design strategy for interweaving quasi-one-dimensional magnetic Tb zigzag chains with non-magnetic Ti-based kagome bilayers in TbTi 3 Bi 4 . Comprehensive spectroscopic analyses reveal coexisting elliptical-spiral magnetic and spin-density-wave orders accompanied by a large ~90 meV band-folding gap. The combined magnetic and electronic state leads to a giant anomalous Hall conductivity of 10 5 Ω −1 cm −1 , which exceeds that observed in frustrated kagome analogues. These results establish TbTi 3 Bi 4 as a model system of magnetic kagome metals with strong electron–magnetism interactions and underscore the necessity of interweaving designed magnetic and charge layers separately to achieve tunable transport properties. This design strategy will enable the discovery of emergent quantum states and next-generation electronic materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Review of honeycomb-based Kitaev materials with zigzag magnetic ordering

The search for a Kitaev quantum spin liquid in crystalline magnetic materials has fueled intense interest in the two-dimensional honeycomb systems. Many promising candidate Kitaev systems are characterized by a long-range-ordered magnetic structure with an antiferromagnetic zigzag-type order, where the static moments form alternating ferromagnetic chains. Recent experiments on high-quality single crystals uncovered the existence of intriguing multi-k magnetic structures, which evolved from zigzag structures. Those discoveries have sparked new theoretical developments and amplified interest in these materials. We present an overview of the honeycomb materials known to display this type of magnetic structure and provide detailed crystallographic information for the possible single- and multi-k variants.

36 MATERIALS SCIENCE↗
DOE OSTI2026

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Discovery of charge density wave in a kagome lattice antiferromagnet

A hallmark of strongly correlated quantum materials is the rich phase diagram resulting from competing and intertwined phases with nearly degenerate ground state energies. A well-known example is the copper oxides, where a charge density wave (CDW) is ordered well above and strongly coupled to the magnetic order to form spin-charge separated stripes that compete with superconductivity. Recently, such rich phase diagrams have also been revealed in correlated topological materials. In two-dimensional kagome lattice metals consisting of corner-sharing triangles, the geometry of the lattice can produce flat bands with localized electrons, non-trivial topology, chiral magnetic order, superconductivity and CDW order. While CDW has been found in weakly electron correlated nonmagnetic AV 3 Sb 5 (A = K, Rb, Cs), it has not yet been observed in correlated magnetic ordered kagome lattice metals. Here we report the discovery of CDW within the antiferromagnetic (AFM) ordered phase of kagome lattice FeGe. The CDW in FeGe occurs at wavevectors identical to that of AV 3 Sb 5 , enhances the AFM ordered moment, and induces an emergent anomalous Hall effect. Furthermore, our findings suggest that CDW in FeGe arises from the combination of electron correlations-driven AFM order and van Hove singularities-driven instability possibly associated with a chiral flux phase, in stark contrast to strongly correlated copper oxides and nickelates, where the CDW precedes or accompanies the magnetic order.

36 MATERIALS SCIENCE↗